<body>
<header id="title-block-header">
<h1 class="title"><div class="line-block">Opportunity Insights Economic Tracker<br />
Data Dictionary</div></h1>
<p class="subtitle">last updated on 2023-08-29</p>
</header>
<p><a href="https://raw.githubusercontent.com/OpportunityInsights/EconomicTracker/main/docs/oi_tracker_data_dictionary.pdf"><img src="pdf-icon.svg" alt="PDF Download" width="50" style="display:inline;"/> Click here to download a PDF version of this document</a></p>

<h2 id="overview">Overview</h2>
<p>Each data source and level of aggregation has a separate CSV, named using the following convention: <em>Data source</em> – <em>Geographic Level of Aggregation</em> – <em>Temporal Level of Aggregation</em></p>
<p>Additionally, we have three files, <strong>GeoIDs – State</strong> and <strong>GeoIDs – County</strong> and <strong>GeoIDs – City</strong>, that provide information on geographic crosswalks and aggregation. These can be merged to any file sharing the same geographic level of aggregation using the geographic identifier. Additionally, <strong>GeoIDs – County</strong> indicates the commuting zone (CZ) and state that each county belongs to. The City-level data (listed under “Metro” on the tracker site) associates the largest cities in the United States with a representative county one-to-one (except in the case of New York City which includes the 5 boroughs).</p>
<p>Finally, we have gathered a collection of key state-level policy dates relevant for changes in other series trends and values. These are contained in the <strong>Policy Milestones – State</strong> file.</p>
<p>A description of the columns in each file follows.</p>
<h2 id="geoid-file-descriptions">GeoID File Descriptions</h2>
<h3 id="geoids---state.csv">GeoIDs - State.csv</h3>
<p>Geographic identifier: <code>statefips</code></p>
<ul>
<li><code>statename</code>: The name of the state.</li>
<li><code>stateabbrev</code>: The 2-letter state abbreviation.</li>
<li><code>state_pop2019</code>: The population of the state in 2019, from Census Bureau estimates.</li>
</ul>
<h3 id="geoids---county.csv">GeoIDs - County.csv</h3>
<p>Geographic identifier: <code>countyfips</code></p>
<ul>
<li><code>countyname</code>: The name of the county.</li>
<li><code>cityid</code>: The city identifier that the county is assigned to.</li>
<li><code>cityname</code>: The name of the city that the county is assigned to.</li>
<li><code>cz</code>: The numeric identifier of the commuting zone (CZ) in which the county is contained.</li>
<li><code>czname</code>: The name of the commuting zone (CZ) in which the county is contained.</li>
<li><code>statename</code>: The name of the state in which the county is contained.</li>
<li><code>statefips</code>: The FIPS code of the state in which the county is contained.</li>
<li><code>stateabbrev</code>: The 2-letter abbreviation of the state in which the county is contained.</li>
<li><code>county_pop2019</code>: The population of the county in 2019 according to Census Bureau estimates.</li>
</ul>
<h3 id="geoids---city.csv">GeoIDs - City.csv</h3>
<p>Geographic identifier: <code>cityid</code></p>
<ul>
<li><code>cityname</code>: The name of the city.</li>
<li><code>stateabbrev</code>: The 2-letter abbreviation of the primary state in which the city is contained.</li>
<li><code>statename</code>: The name of the primary state in which the city is contained.</li>
<li><code>statefips</code>: The FIPS code of the primary state in which the city is contained.</li>
<li><code>lat</code>: Latitude of the city.</li>
<li><code>lon</code>: Longitude of the city.</li>
<li><code>city_pop2019</code>: The population of the city in 2019 according to Census Bureau estimates, calculated as population of the county or counties assigned to the city.</li>
</ul>
<h2 id="data-file-descriptions">Data File Descriptions</h2>
<h3 id="affinity">Affinity</h3>
<p>Credit/debit card spending data from <a href="https://www.affinity.solutions">Affinity Solutions</a>.</p>
<ul>
<li><code>spend_all</code>: Spending in all merchant category codes (MCCs).
<ul>
<li><code>spend_all_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
<li><code>spend_all_incmiddle</code>: …by consumers living in ZIP codes with median income in the middle two quartiles.</li>
</ul></li>
<li><code>spend_aap</code>: Spending in apparel and accessories (AAP) MCCs.
<ul>
<li><code>spend_aap_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_acf</code>: Spending in accomodation and food service (ACF) MCCs.
<ul>
<li><code>spend_acf_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_aer</code>: Spending in arts, entertainment, and recreation (AER) MCCs.
<ul>
<li><code>spend_aer_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_apg</code>: Spending in general merchandise stores (GEN) and apparel and accessories (AAP) MCCs.
<ul>
<li><code>spend_apg_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_gen</code>: Spending in general merchandise stores (GEN) MCCs.
<ul>
<li><code>spend_gen_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_grf</code>: Spending in grocery and food store (GRF) MCCs.
<ul>
<li><code>spend_grf_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_hcs</code>: Spending in health care and social assistance (HCS) MCCs.
<ul>
<li><code>spend_hcs_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_hic</code>: Spending in home improvement centers (HIS) MCCs.
<ul>
<li><code>spend_hic_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_sgh</code>: Spending in sporting goods and hobby (SGH) MCCs.
<ul>
<li><code>spend_sgh_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_tws</code>: Spending in transportation and warehousing (TWS) MCCs.
<ul>
<li><code>spend_tws_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_retail_w_grocery</code>: Spending in retail (BLD, CLO, ELC, FBS, FUR, GEN, SPO) MCCs including grocery spending.
<ul>
<li><code>spend_retail_w_grocery_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_retail_no_grocery</code>: Spending in retail (BLD, CLO, ELC, FUR, GEN, SPO) MCCs excluding grocery spending.
<ul>
<li><code>spend_retail_no_grocery_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_durables</code>: Spending in durable goods (BLD, ELC, FUR, SPO, TEL, VEH) MCCs.
<ul>
<li><code>spend_durables_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_nondurables</code>: Spending in non-durable goods (CLO, FBS, GAS, GEN, HPC, MSC, WHO) MCCs.
<ul>
<li><code>spend_nondurables_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_remoteservices</code>: Spending in remote services (ADM, EDU, FIN, INF, NSR, PST, PUB, UCM) MCCs.
<ul>
<li><code>spend_remoteservices_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_inperson</code>: Spending in in-person services (ACF, HCS, AER, TWS, REN, REP, PLS) MCCs.
<ul>
<li><code>spend_inperson_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>spend_inpersonmisc</code>: Spending in other in-person services (REN, REP, PLS) MCCs.
<ul>
<li><code>spend_inpersonmisc_q#</code>: …by consumers living in ZIP codes with median income in quartile <code>#</code>.</li>
</ul></li>
<li><code>provisional</code>: Indicator to mark that the date is within the most recent three weeks of data and is subject to non-negligible changes as new data is posted.</li>
<li><code>freq</code>: Marks whether the data represents a daily (“d”) or weekly (“w”) value.</li>
</ul>
<!-- List additional variables in comments so they are detected by `verify-csv-columns.py`
  - `industry`:
  - `income_quartile`:
  - `spend_s_all`:
  - `spend_s_all_q#`:
  - `spend_s_all_incmiddle`:
  - `spend_s_aap`:
  - `spend_s_acf`:
  - `spend_s_aer`:
  - `spend_s_apg`:
  - `spend_s_durables`:
  - `spend_s_nondurables`:
  - `spend_s_remoteservices`:
  - `spend_s_inperson`:
  - `spend_s_inpersonmisc`:
  - `spend_s_gen`:
  - `spend_s_grf`:
  - `spend_s_hcs`:
  - `spend_s_hic`:
  - `spend_s_sgh`:
  - `spend_s_tws`:
  - `spend_s_retail_w_grocery`:
  - `spend_s_retail_no_grocery`:
  - `spend_19_all`:
  - `spend_19_all_q#`:
  - `spend_19_all_incmiddle`:
  - `spend_19_aap`:
  - `spend_19_acf`:
  - `spend_19_aer`:
  - `spend_19_apg`:
  - `spend_19_gen`:
  - `spend_19_grf`:
  - `spend_19_hcs`:
  - `spend_19_hic`:
  - `spend_19_sgh`:
  - `spend_19_tws`:
  - `spend_19_inperson`:
  - `spend_19_inpersonmisc`:
  - `spend_19_durables`:
  - `spend_19_nondurables`:
  - `spend_19_remoteservices`:
  - `spend_19_retail_no_grocery`:
  - `spend_19_retail_w_grocery`:
  - `daily_spend_19_all`:
  - `daily_spend_19_q1`:
  - `daily_spend_19_q2`:
  - `daily_spend_19_q3`:
  - `daily_spend_19_q4`:
  - `share_jan2019`:
  - `share_jan2020`:
  - `share_decline_covidfirstwave`:
-->
<p>All spending variables are measured relative to January 6 to February 2, 2020, seasonally adjusted, and calculated as a 7 day moving average. When we subdivide by income using the median income of the ZIP codes, <code>q1</code> is the quartile with the lowest median income and <code>q4</code> is the quartile with the highest median income. At the national level, we release a variety of breakdowns <em>without seasonal adjustment</em> in variables that begin with <code>spend_s_</code> (relative to January 2019 for 2019 data, relative to January 2020 for 2020 data onward) or <code>spend_19_</code> (relative to Janurary 7 to February 3, 2019 for all data) instead of <code>spend_</code>.</p>
<p>The merchant category codes (MCC) making up the grouped spending categories are:</p>
<ul>
<li><strong>Retail spending:</strong> CLO clothing and clothing accessories; BLD building materials, garden equipment, and supplies; ELC electronics and appliances; FBS food and beverage stores; FUR furniture and home furnishings; GEN general merchandise stores; and SPO sporting goods, hobbies, musical instruments, and book stores.</li>
<li><strong>Durable goods:</strong> BLD building materials, gardening equipment, and supplies; ELC electronics and appliances; FUR furniture and home furnishings; SPO sporting goods, hobbies, musical instruments, and bookstores; TEL telecommunications; and VEH motor vehicles and parts.</li>
<li><strong>Non-durable goods:</strong> CLO clothing and clothing accessories; FBS food and beverage stores; GEN general merchandise; HPC health and personal care stores; and WHO wholesale trade.</li>
<li><strong>Remote services:</strong> ADM administrative and support and waste management and remediation services; EDU education; FIN finance and insurance; INF information; PST professional, scientific, and technical; PUB public administration; and UCM utilities, construction, and manufacturing.</li>
<li><strong>In-person services:</strong> ACF accomodation and food services; HCS healthcare and social assistance; AER arts, entertainment, and recreation; TWS transportation and warehousing; REN rental and leasing; REP repair and maintenance; and PLS personal and laundry services.</li>
</ul>
<p>In addition, four supplemental files are included (see the <a href="https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.md">documentation</a> for more details on these files):</p>
<ul>
<li><em>Affinity Income Shares - National - 2019.csv</em></li>
<li><em>Affinity Income Shares - National - 2020.csv</em></li>
<li><em>Affinity Industry Composition - National - 2020.csv</em></li>
<li><em>Affinity Daily Total Spending - National - Daily.csv</em></li>
</ul>
<h3 id="job-postings">Job Postings</h3>
<p>Job postings data from <a href="https://lightcast.io/">Lightcast</a> (formerly known as Burning Glass Technologies).</p>
<ul>
<li><code>bg_posts</code>: Average level of job postings relative to January 4 to 31, 2020.</li>
<li><code>bg_posts_ss30</code>: Average level of job postings relative to January 4 to 31, 2020 in manufacturing (NAICS supersector 30).</li>
<li><code>bg_posts_ss55</code>: Average level of job postings relative to January 4 to 31, 2020 in financial activities (NAICS supersector 55).</li>
<li><code>bg_posts_ss60</code>: Average level of job postings relative to January 4 to 31, 2020 in professional and business services (NAICS supersector 60).</li>
<li><code>bg_posts_ss65</code>: Average level of job postings relative to January 4 to 31, 2020 in education and health services (NAICS supersector 65).</li>
<li><code>bg_posts_ss70</code>: Average level of job postings relative to January 4 to 31, 2020 in leisure and hospitality (NAICS supersector 70).</li>
<li><code>bg_posts_jz1</code>: Average level of job postings relative to January 4 to 31, 2020 requiring little/no preparation (ONET jobzone level 1).</li>
<li><code>bg_posts_jz2</code>: Average level of job postings relative to January 4 to 31, 2020 requiring some preparation (ONET jobzone level 2).</li>
<li><code>bg_posts_jz3</code>: Average level of job postings relative to January 4 to 31, 2020 requiring medium preparation (ONET jobzone level 3).</li>
<li><code>bg_posts_jz4</code>: Average level of job postings relative to January 4 to 31, 2020 requiring considerable preparation (ONET jobzone level 4).</li>
<li><code>bg_posts_jz5</code>: Average level of job postings relative to January 4 to 31, 2020 requiring extensive preparation (ONET jobzone level 5).</li>
<li><code>bg_posts_jzgrp12</code>: Average level of job postings relative to January 4 to 31, 2020 requiring low preparation (ONET jobzone levels 1 and 2).</li>
<li><code>bg_posts_jzgrp345</code>: Average level of job postings relative to January 4 to 31, 2020 requiring high preparation (ONET jobzone levels 3, 4 and 5).</li>
</ul>
<!-- List additional variables in comments so they are detected by `verify-csv-columns.py`
  - `share_jan2020`: 
  - `industry`:
-->
<p>In addition, the following supplemental file is included (see the <a href="https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.md">documentation</a> for more details on this file):</p>
<ul>
<li><em>Job Postings Industry Shares - National - 2020.csv</em></li>
</ul>
<h3 id="covid">COVID</h3>
<p>COVID cases and deaths numbers are from the <a href="https://github.com/nytimes/covid-19-data">New York Times</a> and the <a href="https://covid.cdc.gov/covid-data-tracker/#datatracker-home">Centers for Disease Control and Prevention</a>, hospitalizations numbers are from the <a href="https://beta.healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh">U.S. Department of Health and Human Services</a>, tests numbers are from <a href="https://github.com/govex/COVID-19">Johns Hopkins University</a>, and vaccination numbers are from the <a href="https://covid.cdc.gov/covid-data-tracker/#datatracker-home">Centers for Disease Control and Prevention</a>.</p>
<ul>
<li><code>case_rate</code>: Confirmed COVID-19 cases per 100,000 people, seven day moving average.
<ul>
<li><code>case_count</code>: Confirmed COVID-19 cases, seven day moving average.</li>
</ul></li>
<li><code>new_case_rate</code>: New confirmed COVID-19 cases per 100,000 people, seven day rolling sum.
<ul>
<li><code>new_case_count</code>: New confirmed COVID-19 cases, seven day rolling sum.</li>
</ul></li>
<li><code>death_rate</code>: Confirmed COVID-19 deaths per 100,000 people, seven day moving average.
<ul>
<li><code>death_count</code>: Confirmed COVID-19 deaths, seven day moving average.</li>
</ul></li>
<li><code>new_death_rate</code>: New confirmed COVID-19 deaths per 100,000 people, seven day rolling sum.
<ul>
<li><code>new_death_count</code>: New confirmed COVID-19 deaths, seven day rolling sum.</li>
</ul></li>
<li><code>test_rate</code>: Confirmed COVID-19 tests per 100,000 people, seven day moving average.
<ul>
<li><code>test_count</code>: Confirmed COVID-19 tests, seven day moving average.</li>
</ul></li>
<li><code>new_test_rate</code>: New confirmed COVID-19 tests per 100,000 people, seven day moving average.
<ul>
<li><code>new_test_count</code>: New confirmed COVID-19 tests, seven day moving average.</li>
</ul></li>
<li><code>vaccine_rate</code>: First vaccine doses administered per 100 people.
<ul>
<li><code>vaccine_count</code>: First vaccine doses administered.</li>
</ul></li>
<li><code>new_vaccine_rate</code>: New first vaccine doses administered per 100 people, seven day moving average.
<ul>
<li><code>new_vaccine_count</code>: New first vaccine doses administered, seven day moving average.</li>
</ul></li>
<li><code>fullvaccine_rate</code>: Vaccine series completed per 100 people.
<ul>
<li><code>fullvaccine_count</code>: Vaccine series completed.</li>
</ul></li>
<li><code>new_fullvaccine_rate</code>: New vaccine series completed per 100 people, seven day moving average.
<ul>
<li><code>new_fullvaccine_count</code>: New vaccine series completed, seven day moving average.</li>
</ul></li>
<li><code>booster_first_rate</code>: First booster doses administered per 100 people.
<ul>
<li><code>booster_first_count</code>: First booster doses administered.</li>
</ul></li>
<li><code>new_booster_first_rate</code>: New first booster doses administered per 100 people, seven day moving average.
<ul>
<li><code>new_booster_first_count</code>: New first booster doses administered, seven day moving average.</li>
</ul></li>
<li><code>hospitalized_rate</code>: New patients currently hospitalized in an inpatient bed who have suspected or confirmed COVID-19 per 100,000 people, seven day moving average.
<ul>
<li><code>hospitalized_count</code>: Newly patients currently hospitalized in an inpatient bed who have suspected or confirmed COVID-19, seven day moving average.</li>
</ul></li>
</ul>
<h3 id="google-mobility">Google Mobility</h3>
<p>GPS mobility data indexed to January 3 to February 6, 2020 from <a href="https://www.google.com/covid19/mobility/">Google COVID-19 Community Mobility Reports</a>.</p>
<ul>
<li><code>gps_away_from_home</code>: Time spent outside of residential locations.</li>
<li><code>gps_retail_and_recreation</code>: Time spent at retail and recreation locations.</li>
<li><code>gps_grocery_and_pharmacy</code>: Time spent at grocery and pharmacy locations.</li>
<li><code>gps_parks</code>: Time spent at parks.</li>
<li><code>gps_transit_stations</code>: Time at inside transit stations.</li>
<li><code>gps_workplaces</code>: Time spent at work places.</li>
<li><code>gps_residential</code>: Time spent at residential locations.</li>
</ul>
<h3 id="employment">Employment</h3>
<p>Employment levels relative to January 4 to 31, 2020 from <a href="https://www.paychex.com/">Paychex</a> and <a href="https://www.intuit.com/">Intuit</a>.</p>
<ul>
<li><code>emp</code>: Employment level for all workers.</li>
<li><code>emp_incq1</code>: Employment level for workers in the bottom quartile of the wage distribution (annualized wage lower than the federal poverty line).</li>
<li><code>emp_incq2</code>: Employment level for workers in the second quartile of the wage distribution (annualized wage between 1x and 1.5x the federal poverty line).</li>
<li><code>emp_incmiddle</code>: Employment level for workers in the middle two quartiles of the wage distribution (annualized wage between 1x and 2.5x the federal poverty line).</li>
<li><code>emp_incq3</code>: Employment level for workers in the third quartile of the wage distribution (annualized wage between 1.5x and 2.5x the federal poverty line).</li>
<li><code>emp_incq4</code>: Employment level for workers in the top quartile of the wage distribution (annualized wage greater than 2.5x the federal poverty line).</li>
<li><code>emp_incbelowmed</code>: Employment level for workers in the bottom half of the wage distribution (annualized wage less than 1.5x the federal poverty line).</li>
<li><code>emp_incabovemed</code>: Employment level for workers in the top half of the wage distribution (annualized wage greater than 1.5x the federal poverty line).</li>
<li><code>emp_ss40</code>: Employment level for workers in trade, transportation and utilities (NAICS supersector 40).</li>
<li><code>emp_ss60</code>: Employment level for workers in professional and business services (NAICS supersector 60).</li>
<li><code>emp_ss65</code>: Employment level for workers in education and health services (NAICS supersector 65).</li>
<li><code>emp_ss70</code>: Employment level for workers in leisure and hospitality (NAICS supersector 70).</li>
<li><code>emp_retail</code>: Employment level for workers in retail (NAICS sector 44-45).</li>
<li><code>emp_retail_inclow</code>: Employment level for workers in retail (NAICS sector 44-45) and in the bottom quartile of the wage distribution (annualized wage lower than the federal poverty line).</li>
<li><code>emp_retail_incmiddle</code>: Employment level for workers in retail (NAICS sector 44-45) and in the middle two quartiles of the wage distribution (annualized wage between 1x and 2.5x the federal poverty line).</li>
<li><code>emp_retail_inchigh</code>: Employment level for workers in retail (NAICS sector 44-45) and in the top quartile of the wage distribution (annualized wage greater than 2.5x the federal poverty line).</li>
<li><code>emp_s72</code>: Employment level for workers in accommodation and food services (NAICS sector 72).</li>
<li><code>emp_subset_unweighted_q1</code>: Employment level for workers in the bottom quartile of the wage distribution (annualized wage lower than the federal poverty line) in county x industry (2-digit NAICS code) cells with nonzero employment for all four wage quartiles.</li>
<li><code>emp_subset_unweighted_q2</code>: Employment level for workers in the second quartile of the wage distribution (annualized wage between 1x and 1.5x the federal poverty line) in county x industry (2-digit NAICS code) cells with nonzero employment for all four wage quartiles.</li>
<li><code>emp_subset_unweighted_q3</code>: Employment level for workers in the third quartile of the wage distribution (annualized wage between 1.5x and 2.5x the federal poverty line) in county x industry (2-digit NAICS code) cells with nonzero employment for all four wage quartiles.</li>
<li><code>emp_subset_unweighted_q4</code>: Employment level for workers in the top quartile of the wage distribution (annualized wage greater than 2.5x the federal poverty line) in county x industry (2-digit NAICS code) cells with nonzero employment for all four wage quartiles.</li>
<li><code>emp_subset_reweighted_q1</code>: Employment level for workers in the bottom quartile of the wage distribution (annualized wage lower than the federal poverty line) in county x industry cells with nonzero employment for all four wage quartiles, reweighting to match the county x industry (2-digit NAICS code) distribution of workers in the top quartile of the wage distribution.</li>
<li><code>emp_subset_reweighted_q2</code>: Employment level for workers in the second quartile of the wage distribution (annualized wage between 1x and 1.5x the federal poverty line) in county x industry cells with nonzero employment for all four wage quartiles, reweighting to match the county x industry (2-digit NAICS code) distribution of workers in the top quartile of the wage distribution.</li>
<li><code>emp_subset_reweighted_q3</code>: Employment level for workers in the third quartile of the wage distribution (annualized wage between 1.5x and 2.5x the federal poverty line) in county x industry cells with nonzero employment for all four wage quartiles, reweighting to match the county x industry (2-digit NAICS code) distribution of workers in the top quartile of the wage distribution.</li>
<li><code>emp_subset_reweighted_q4</code>: Employment level for workers in the top quartile of the wage distribution (annualized wage greater than 2.5x the federal poverty line) in county x industry cells with nonzero employment for all four wage quartiles, reweighting to match the county x industry (2-digit NAICS code) distribution of workers in the top quartile of the wage distribution.</li>
</ul>
<!-- List additional variables in comments so they are detected by `verify-csv-columns.py`
  - `emp_incq1_apr2020`:
  - `emp_incq1_jul2020`:
-->
<p>In addition, the following supplemental file is included (see the <a href="https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.md">documentation</a> for more details on this file):</p>
<ul>
<li><em>Earnin - ZCTA - 2020.csv</em></li>
</ul>
<h3 id="ui-claims">UI Claims</h3>
<p>Unemployment insurance claims data from the <a href="https://oui.doleta.gov/unemploy/DataDashboard.asp">Department of Labor</a> (national and state-level) and numerous individual state agencies (county-level).</p>
<ul>
<li><code>initclaims_rate_regular</code>: Number of initial claims per 100 people in the 2019 labor force, Regular UI only
<ul>
<li><code>initclaims_count_regular</code>: Count of initial claims, Regular UI only</li>
</ul></li>
<li><code>initclaims_rate_pua</code>: Number of initial claims per 100 people in the 2019 labor force, PUA (Pandemic Unemployment Assistance) only
<ul>
<li><code>initclaims_count_pua</code>: Count of initial claims, PUA (Pandemic Unemployment Assistance) only</li>
</ul></li>
<li><code>initclaims_rate_combined</code>: Number of initial claims per 100 people in the 2019 labor force, combining Regular and PUA claims
<ul>
<li><code>initclaims_count_combined</code>: Count of initial claims, combining Regular and PUA claims</li>
</ul></li>
<li><code>contclaims_rate_regular</code>: Number of continued claims per 100 people in the 2019 labor force, Regular UI only
<ul>
<li><code>contclaims_count_regular</code>: Count of continued claims, Regular UI only</li>
</ul></li>
<li><code>contclaims_rate_pua</code>: Number of continued claims per 100 people in the 2019 labor force, PUA (Pandemic Unemployment Assistance) only
<ul>
<li><code>contclaims_count_pua</code>: Count of continued claims, PUA (Pandemic Unemployment Assistance) only</li>
</ul></li>
<li><code>contclaims_rate_peuc</code>: Number of continued claims per 100 people in the 2019 labor force, PEUC (Pandemic Emergency Unemployment Compensation) only
<ul>
<li><code>contclaims_count_peuc</code>: Count of continued claims, PEUC (Pandemic Emergency Unemployment Compensation) only</li>
</ul></li>
<li><code>contclaims_rate_combined</code>: Number of continued claims per 100 people in the 2019 labor force, combining Regular, PUA and PEUC claims
<ul>
<li><code>contclaims_count_combined</code>: Count of continued claims, combining Regular, PUA and PEUC claims</li>
</ul></li>
</ul>
<h3 id="womply">Womply</h3>
<p>Small business openings and revenue data from <a href="https://www.womply.com/">Womply</a>.</p>
<ul>
<li><code>merchants_all</code>: Percent change in number of small businesses open, calculated as a seven-day moving average, seasonally adjusted, and indexed to January 4 to 31, 2020.
<ul>
<li><code>merchants_inchigh</code>: … in high income (quartile 4 of median income) ZIP codes.</li>
<li><code>merchants_incmiddle</code>: … in middle income (quartiles 2 &amp; 3 of median income) ZIP codes.</li>
<li><code>merchants_inclow</code>: … in low income (quartile 1 of median income) ZIP codes.</li>
<li><code>merchants_retail</code>: … in retail businesses (NAICS 2-digit codes 44-45).</li>
<li><code>merchants_food_accommodation</code>: … in food and accommodation businesses (NAICS 2-digit code 72)</li>
<li><code>merchants_professional</code>: … in professional services businesses (NAICS 2-digit code 54)</li>
<li><code>merchants_other_services</code>: … in other services businesses (NAICS 2-digit code 81)</li>
<li><code>merchants_health</code>: … in health &amp; social service businesses (NAICS 2-digit code 62)</li>
</ul></li>
<li><code>revenue_all</code>: Percent change in net revenue for small businesses, calculated as a seven-day moving average, seasonally adjusted, and indexed to January 4 to 31, 2020.
<ul>
<li><code>revenue_inchigh</code>: … in high income (quartile 4 of median income) zipcodes.</li>
<li><code>revenue_incmiddle</code>: … in middle income (quartiles 2 &amp; 3 of median income) zipcodes.</li>
<li><code>revenue_inclow</code>: … in low income (quartile 1 of median income) zipcodes.</li>
<li><code>revenue_retail</code>: … in retail businesses (NAICS 2-digit codes 44-45).</li>
<li><code>revenue_food_accommodation</code>: … in food and accommodation businesses (NAICS 2-digit code 72).</li>
<li><code>revenue_professional</code>: … in professional services businesses (NAICS 2-digit code 54).</li>
<li><code>revenue_other_services</code>: … in other services businesses (NAICS 2-digit code 81)</li>
<li><code>revenue_health</code>: … in health &amp; social service businesses (NAICS 2-digit code 62)</li>
</ul></li>
</ul>
<!-- List additional variables in comments so they are detected by `verify-csv-columns.py`
  - `revenue_all_apr2020`:
  - `revenue_all_jul2020`:
-->
<p>In addition, the following supplemental file is included (see the <a href="https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.md">documentation</a> for more details on this file):</p>
<ul>
<li><em>Womply - ZCTA - 2020.csv</em></li>
</ul>
<h3 id="zearn">Zearn</h3>
<p>Online math learning data from <a href="https://www.zearn.org/">Zearn</a>.</p>
<ul>
<li><code>engagement</code>: Average level of students using platform relative to January 6 to February 21, 2020.</li>
<li><code>engagement_inclow</code>: Average level of students using platform relative to January 6 to February 21, 2020 for schools in the 25% of ZIP codes with the lowest median income.</li>
<li><code>engagement_incmiddle</code>: Average level of students using platform relative to January 6 to February 21, 2020 for schools in ZIP codes with median income between the 25th and 75th percentiles.</li>
<li><code>engagement_inchigh</code>: Average level of students using platform relative to January 6 to February 21, 2020 for schools in the 25% of ZIP codes with the highest median income.</li>
<li><code>badges</code>: Average level of student achievements earned (badges) on platform relative to January 6 to February 21, 2020.</li>
<li><code>badges_inclow</code>: Average level of student achievements earned (badges) on platform relative to January 6 to February 21, 2020 for schools in the 25% of ZIP codes with the lowest median income.</li>
<li><code>badges_incmiddle</code>: Average level of student achievements earned (badges) on platform relative to January 6 to February 21, 2020 for schools in ZIP codes with median income between the 25th and 75th percentiles.</li>
<li><code>badges_inchigh</code>: Average level of student achievements earned (badges) on platform relative to January 6 to February 21, 2020 for schools in the 25% of ZIP codes with the highest median income. <!-- List additional variables in comments so they are detected by `verify-csv-columns.py`
- `break_engagement`:
- `break_badges`:
- `break_engagement_inchigh`:
- `break_badges_inchigh`:
- `break_engagement_inclow`:
- `break_badges_inclow`:
- `break_engagement_incmiddle`:
- `break_badges_incmiddle`:
- `imputed_from_cz`:
--></li>
</ul>
<p>Note that for every variable listed here, there is a corresponding variable with the prefix <code>break_</code> (for example, <code>break_engagement</code>). During the period in which schools are on summer or winter break, we record the outcomes in these <code>break_</code> variables instead of the usual variables. These numbers are not displayed on the <a href="https://tracktherecovery.org">Economic Tracker</a> because they do not reliably measure differences in student learning across geography and income groups when many schools are on break.</p>
<p>To ensure privacy, the results for some counties are masked. Where possible, masked county levels values are replaced by commuting zone means, as indicated by the <code>imputed_from_cz</code> variable. The masking criteria are explained in further detail in our <a href="https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.md#online-math-participation">data documentation</a>.</p>
<h2 id="policy-milestones">Policy Milestones</h2>
<p>Key state-level policy dates relevant for changes in other series trends and values.</p>
<ul>
<li><code>statefips</code>: 2-digit FIPS code of the U.S. state a milestone was recorded in</li>
<li><code>date</code>: Date the policy milestone occurred</li>
<li><code>policy_description</code>: Description of policy milestone</li>
<li><code>schools_first_closed</code>: Whether a milestone is the date at which the state first ordered all public K-12 schools statewide to physically close</li>
<li><code>nonessential_biz_first_closed</code>: Whether a milestone is the date on which the state government first ordered all nonessential businesses to close statewide</li>
<li><code>stayathome_first_start</code>: Whether a milestone is the date on which the state government first told residents to stay home, save for excepted activities statewide</li>
</ul>
</body>
